جست‌وجوی استاد و پژوهشگر

Surjo R. Soekadarمشاهده پروفایل

استاد

• Clinical Neurotechnology
• Brain-Computer Interfaces (BCI)
• Neuromodulation
• +۸ مورد دیگر

Surjo R. Soekadar is the Einstein Professor of Clinical Neurotechnology at Charité – University Medicine Berlin. He leads the Clinical Neurotechnology Laboratory , which focuses on developing noninvasive neurotechnologies for treating neurological and psychiatric disorders through closed-loop brain stimulation and advanced brain-machine interfaces (BCI/BMI). His work integrates real-time EEG/MEG monitoring with electromagnetic stimulation to modulate pathological brain oscillations and enhance neuroplasticity in conditions like stroke, spinal cord injury, and psychiatric disorders. Education : Studied medicine in Mainz, Heidelberg, and Baltimore Clinical Training : Residency in Psychiatry and Psychotherapy at University of Tübingen Academic Journey : 2008-2011 Research Fellow at NINDS (USA); 2017 Venia Legendi at University of Tübingen; 2018 First Professor of Clinical Neurotechnology in Germany His research interests span: • Closed-loop neurostimulation combining real-time brain state monitoring with targeted intervention • Next-generation BCI using optically pumped magnetometers (OPM) for mobile MEG recordings • Neurorehabilitation through exoskeleton control and sensory feedback • Neurophysiological modeling of entropy measures and phase flows Recent publications highlight: • Adaptive deep brain stimulation protocols • Real-time phase-sensitive tACS applications • OPM-based BCI innovations • Stroke recovery mechanisms through corticospinal tract analysis Scientific recognition includes: International BCI Research Award BIOMAG Award NARSAD Young Investigator Award Funded by the European Research Council (ERC) , his lab trains doctoral students like David Haslacher (EEG/MEG integration), Khaled Nasr (multicoil TMS optimization), and Annalisa Colucci (entropy-driven BCI development). The team also explores quantum AI applications in clinical decision-making and bidirectional BCI systems using OPM and tES.

Gordon Chengمشاهده پروفایل

استاد

• Neuroengineering
• Robotics
• NeuroRobotics
• +۱ مورد دیگر

Professor Gordon Cheng is a faculty member at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology (CIT). He serves as the Director of the Chair of Cognitive Systems at the Institute for Cognitive Systems (ICS), where he focuses on advancing cognitive systems, robotics, and their intersections. His research addresses fundamental challenges in creating intelligent systems capable of understanding and interacting with complex environments. Research Interests: His work spans Neuroengineering , Robotics , and Cognitive Systems , emphasizing the integration of biological principles into artificial systems. Key projects include robot skin development , EEG-based prosthetic control , and human-robot co-adaptation . Selected Publications: Recent articles highlight advancements in real-time tactile sensing , neuroprosthetics , and BCI-driven rehabilitation , reflecting his interdisciplinary approach bridging robotics and neuroscience. Supervision: He supervises PhD students from the Graduate School of Neuroscience (GSN), including Jasmin Kajopoulos and Nikolas Berberich. Contact: Email: gordon@tum.de | Website

Kai Essigمشاهده پروفایل

استاد

• Human Factors
• Interactive Systems
• Eye Tracking
• +۱۵ مورد دیگر

Prof. Dr. Kai Essig is a Professor of Human Factors and Interactive Systems at the Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Kamp-Lintfort, Germany. He has a strong interdisciplinary background combining computer science, cognitive science, and human-computer interaction, with a focus on eye tracking, visual perception, and assistive technologies. Master of Science in Computer Science and Chemistry, Bielefeld University (1998) Ph.D. in Computer Science, Bielefeld University (2007) His research centers on eye tracking, human-computer interaction, usability engineering, visual attention, and cognitive interaction technology . He investigates how movement expertise influences visual perception and how multimodal software can support real-time human actions. His work integrates computer vision, machine learning, and neuroscience to develop intelligent systems that adapt to user behavior. The 15 most recent publications reflect a consistent trend in eye movement analysis, mental representations, brain-machine interfaces, and assistive technologies . These works span domains such as sports psychology, robotics, augmented reality, and cognitive neuroscience, demonstrating a strong interdisciplinary approach. Key themes include gaze-based interaction, automated annotation of visual behavior, and the implementation of smart systems for daily living assistance. Scientific recognition includes: Landmark in the Land of Ideas (2018) – for the ADAMAAS project, awarded by 'Land of Ideas', a joint initiative of the German government and the Federation of German Industries Prof. Essig has been actively involved in research projects such as ADAMAAS (Adaptive and Mobile Action Assistance in Daily Living Activities), which received national recognition. He has collaborated extensively with the Neurocognition and Action-Biomechanics Research Group at Bielefeld University and the Excellence Cluster CITEC. While no formal advising of students is listed, his publications suggest mentorship and collaboration with junior researchers. His lab work is centered on eye-tracking systems, multimodal interaction, and cognitive modeling , particularly within applied environments like smart glasses and assistive technologies.

Sandra Diaz Pierمشاهده پروفایل

پژوهشگر

• Computational Neuroscience
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Sandra Diaz Pier is a Scientific Lead at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre , Germany. Specializing in computational neuroscience , high performance computing (HPC) , and machine learning , she bridges neuroscience and advanced computational methods through her research. Education: B.Sc. in Electronic Systems Engineering, Mexico M.Sc. in Computer Science (focus: machine learning, quantum computing), Mexico Second M.Sc. in Electrical Engineering, Ontario, Canada Ph.D. in Computer Science, Germany (2021) Her research focuses on modeling and simulating brain dynamics and plasticity at multiple scales, leveraging HPC to accelerate large-scale neural network simulations. She actively contributes to EU projects like the Human Brain Project (HBP) , Virtual Brain Cloud , and EBRAINS 2.0 , emphasizing infrastructure development and educational training. Her work includes open-source tools such as the NEST simulator , The Virtual Brain , and L2L , enabling efficient parameter exploration and multiscale co-simulation frameworks. The 15 most recent publications highlight her interdisciplinary approach, spanning topics from quantum computing in biomolecular simulations to neural plasticity algorithms and cloud-based brain modeling . These articles reflect her expertise in integrating machine learning , multi-scale simulation , and HPC infrastructure for neuroscience challenges, including seizure propagation, Parkinson’s disease progression, and swarm intelligence in spiking networks. She leads technical coordination in projects like EBRAINS and serves as a task leader in the HBP infrastructure work package , while also organizing workshops and hackathons for open-source tools. Her role involves supporting domain scientists through methodological research and workflow optimization for brain simulations.

• Cognitive Science
• Biomechanics
• Sports Psychology
• +۳ مورد دیگر

Prof. Dr. Thomas Schack is a faculty member in the Department of Sport Science at Bielefeld University , specifically within the Faculty of Psychology and Sport Science and the Center for Cognitive Interaction Technology (CITEC) . His research focuses on the intersection of Neurocognition and Movement - Biomechanics , with significant contributions to understanding motor learning, cognitive representations in sports, and neurophysiological adaptations. Key Research Areas : Academic resilience, EEG neurofeedback applications, sport emotion validation, motor imagery classification, and balanceability enhancement through spinal cord stimulation. Notable Methodologies : Psychometric validation across cultures, longitudinal studies on student engagement, and ERP/EEG studies on motor planning and execution. Impact : His work bridges sports science with cognitive neuroscience, emphasizing cross-cultural applications (e.g., Ghanaian educational contexts) and technological interventions in athletic performance. Publications show a strong emphasis on academic resilience in educational settings, motor skill acquisition through neurocognitive frameworks, and cross-cultural psychometric validation. His recent studies analyze balanceability support systems and expertise-dependent cognitive performance in sports and chess.

Jun Morimotoمشاهده پروفایل

استاد

• Reinforcement Learning
• Humanoid Robotics
• Exoskeleton Robotics
• +۳ مورد دیگر

Jun Morimoto is a Professor and Head of the Department of Brain Robot Interface at ATR Computational Neuroscience Laboratories. He holds a Ph.D. in Information Science from the Nara Institute of Science and Technology (NAIST) and has held positions at Carnegie Mellon University and the Japan Science and Technology Agency (JST). His research focuses on reinforcement learning, humanoid robotics, exoskeleton systems, and brain-robot interfaces. He has led teams at RIKEN and contributed to projects such as the ICORP initiative. His work integrates neuroscience principles with robotics, emphasizing applications in assistive technologies and neurorehabilitation. Key contributions include developing exoskeleton control strategies, brain-computer interfaces, and adaptive humanoid robot systems. He has authored over 100 peer-reviewed papers, with recent work addressing EEG-based motor intent decoding and multi-site neuroimaging databases. His academic service includes organizing international conferences (e.g., Humanoid Robots, ICRA) and serving on program committees. He has also delivered invited talks on topics such as brain-controlled exoskeletons and stochastic optimal control in robotics.

Nitish V. Thakorمشاهده پروفایل

استاد

• Biomedical Engineering
• Neuroengineering
• Neural Prosthetics
• +۵ مورد دیگر

Nitish V. Thakor is a Professor of Biomedical Engineering and Neurology at Johns Hopkins University School of Medicine, with secondary appointments in Electrical and Computer Engineering. He directs the Laboratory for Neuroengineering and oversees the NIH Neuroengineering Training Grant. His research focuses on neural prosthetics, brain-machine interfaces, and clinical neurorehabilitation technologies. Education: B.Tech from IIT Bombay (India), M.S. and Ph.D. in Biomedical Engineering from University of Wisconsin-Madison Joined Johns Hopkins faculty in 1983 Research emphasizes translational neuroengineering: developing prosthetic limbs with sensory feedback, advanced neural interfaces, and diagnostic technologies for brain injury monitoring. The lab integrates microfabrication, stem cell biology, and integrated circuit design with clinical applications. Notable projects include DARPA-funded advanced prosthetic systems and temperature-sensitive prosthetic fingertips. Recent lab milestones include robotic hand research published in Science Advances, and student achievements like NIH fellowships and Siebel Scholar awards. Honors: IEEE Fellow (1997), AIMBE Fellow (1996), NSF Presidential Young Investigator (1985) Advising over 20 students/postdocs, he also serves as editor-in-chief of Medical and Biological Engineering and Computing . Active in global biomedical engineering outreach through conferences and educational programs.

• Biomedical Signal Processing
• Computational Neuroscience
• Brain-Computer Interfaces (BCI)
• +۷ مورد دیگر

David B. Grayden is a Professor at The University of Melbourne, affiliated with the Melbourne School of Engineering and the Department of Electrical and Electronic Engineering . His work spans Biomedical Signal Processing , Computational Neuroscience , and Brain-Computer Interfaces (BCI) , focusing on applications in Epilepsy Research and Cochlear Implants . Melbourne Neural Engineering Laboratory member Collaborator in multidisciplinary biomedical research Key research interests include: Developing Seizure Prediction Algorithms using long-term EEG/iEEG data Neural mass modeling for Epilepsy and Inhibitory Network Behavior Optimizing Cochlear Implants via computational models Advancing Endovascular BCI Systems and Neural Stimulation Recent publications highlight trends in Machine Learning , Path Signatures , and Multi-Frequency Stimulation for SSVEP-based BCIs . His work integrates Computational Modeling with Biomedical Engineering to address clinical challenges in neuroprosthetics and sensory processing. Grayden leads projects on Neural Network Dynamics , Biomedical Signal Analysis , and Neurostimulation , often collaborating with institutions like Monash University and Royal Melbourne Hospital .

• Signal Processing
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.

Berend Isermannمشاهده پروفایل

استاد

• Thrombo-inflammation
• Molecular Biology
• Immunology
• +۴ مورد دیگر

Professor Berend Isermann is a Professor of Laboratory Medicine at Leipzig University, affiliated with the Faculty of Medicine. He leads research into thrombo-inflammation mechanisms, focusing on molecular pathways linking coagulation and inflammation in diseases such as diabetes, kidney failure, and cardiovascular conditions. His work has earned prestigious recognition, including the ERC Advanced Grant (2024) for a project investigating molecular switches in thrombo-inflammation. Education: Studied medicine at Würzburg (Germany), Bristol (UK), and New Haven (USA). Completed specialist training in internal medicine, endocrinology, and laboratory medicine at Heidelberg University Hospital. Previously held professorships at the Universities of Magdeburg (2011–2019) and Leipzig (2019–present). Research interests center on understanding how thrombo-inflammation drives diseases like preeclampsia, chronic kidney disease, and complications from infections like COVID-19. Key projects include studying tissue factor’s role in inflammation-coagulation cross-talk, microglial-brain interactions in chronic kidney disease, and developing therapies targeting molecular switches. Scientific achievements include discovering tissue factor’s complex with inflammatory regulators, published in high-impact journals, and advancing methods to measure thrombo-inflammatory biomarkers. His interdisciplinary team at Leipzig Medical Center explores therapeutic strategies to control thrombo-inflammation across multiple organ systems. Awards: ERC Advanced Grant (2024) valued at €2.5 million for five years of research into molecular mechanisms of thrombo-inflammation. Grants and collaborations: ERC-funded project involves analyzing molecular switches in heart disease, obesity, and cancer models. His work bridges basic science and clinical translation, with a focus on kidney-brain interactions and diet-based interventions like the green-Mediterranean diet’s epigenetic effects. Labs/Teams: Leads a cross-departmental team at Leipzig University Medical Center integrating immunology, nephrology, and cardiology research. Collaborates on projects involving placental thrombo-inflammation, anticoagulant drug effects, and machine learning applications in diagnostics.

• Computer Vision
• Machine Learning
• Biometrics
• +۳ مورد دیگر

David Menotti is a prominent researcher in computer vision and machine learning, with a focus on biometrics, license plate recognition, and video analysis. He has collaborated extensively with institutions and researchers globally, contributing to over 171 publications between 2003-2025. Key research areas include face recognition, synthetic data generation, and zero-shot learning Major contributions in license plate super-resolution, sign language translation, and ocular biometrics Active in organizing competitions like FRCSyn and OCFR to advance synthetic data applications His work often combines diffusion models, CNN architectures, and multimodal approaches to solve real-world problems in unconstrained environments. Notable recent projects involve enhancing face recognition with synthetic data, vehicle color recognition under adverse conditions, and Libras-to-Portuguese translation. Menotti's publications appear in journals like Information Fusion , IEEE Access , and conferences including CVPR, SIBGRAPI, and IJCNN. He frequently collaborates with researchers such as Rayson Laroca, William Robson Schwartz, and Pedro Vidal.

Aysim Tokerمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Aysim Toker is a Ph.D. candidate at the Chair for Computer Vision and Artificial Intelligence , affiliated with the Technical University of Munich . She works under the supervision of Prof. Dr. Laura Leal-Taixe and Prof. Dr. Xiaoxiang Zhu on interdisciplinary projects. Research Focus: Deep learning, sequence analysis, and remote sensing. Key Contributions: Advancing video object segmentation, Earth observation models, and anonymization techniques. Collaborations: Partnered with international researchers across multiple institutions. Publications span top venues like eLife , ICCV , NeurIPS , and CVPR , with a focus on: Deep learning architectures for multi-modal tasks Image and video analysis in remote sensing and anonymization Tracking and segmentation algorithms Her work bridges foundational computer vision research with applications in Earth science and privacy-preserving technologies.

Nori Jacobyمشاهده پروفایل

استادیار

• Cognitive Psychology
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Nori Jacoby is an Assistant Professor in the Department of Psychology at Cornell University and a Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt. Her research bridges cognitive science, neuroscience, and machine learning to explore how internal representations shape sensory and cognitive abilities, with a focus on universality/diversity in perception and collective behavior. Education: PhD from Hebrew University of Jerusalem (ELSC), postdocs at MIT, UC Berkeley, and Columbia University Lab: Directs the CoCoCo Lab (Cornell Computational Cognition Lab) Funding: NSF-funded postdoctoral program collaborating with UC Davis, CUNY, and Princeton Research interests include: High-dimensional perceptual spaces using adaptive sampling (e.g., Gibbs sampling with people) Cross-cultural studies of music and perception via global experiments Human-AI hybrid systems for collective creativity and decision-making Recent work explores mechanisms of cultural diversity in urban populations, neural correlates of rhythm in stroke patients, and LLM alignment with human sensory judgments. Current projects include large-scale music evolution experiments and NSF-funded studies on collective intelligence. Recruitment: Actively hiring postdocs and PhD students for interdisciplinary work.

Felix Biessmannمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Algorithmic Interpretability
• +۳ مورد دیگر

Professor Felix Biessmann is a faculty member at Berliner Hochschule für Technik (Faculty VI – Computer Science and Media) since October 2018. He studied human cognition at Osnabrück, Zurich, and Tübingen, earned a PhD in Machine Learning from Technische Universität Berlin, and worked as a Machine Learning Scientist at Amazon (2014–2018) and an Assistant Professor at Korea University (2013). His research focuses on machine learning , data science , and algorithmic interpretability , particularly in biomedical applications (e.g., brain-computer interfaces, prosthetics), neuroscience, and social media analysis. He has supervised over 40 graduate theses on topics like TinyML for bird song detection, explainable AI, synthetic data generation, and anomaly detection in time series. Key collaborations include projects with the German Cancer Society (DKG) and Berlin Social Science Center (WZB) . Teaching roles span Python for Data Science, Advanced Machine Learning, Deep Learning, and Scientific Writing at BHT, Korea University, and TU Berlin. Education : PhD (TU Berlin, 2011), MSc (Max Planck Research School, 2007), BSc (University of Osnabrück, 2005) Current Role : Professor of Data Science at Berliner Hochschule für Technik Contact : felix.biessmann@bht-berlin.de | Luxemburger Str. 10, 13353 Berlin

Karin Moraمشاهده پروفایل

پژوهشگر

• Ecology
• Climate Change
• Artificial Intelligence
• +۷ مورد دیگر

Karin Mora is a postdoctoral researcher at Leipzig University , affiliated with the Faculty of Physics and Earth System Sciences . She leads the DeepFeatures project funded by the European Space Agency and contributes to the proposed Breathing Nature excellence cluster, combining biology, economics, meteorology, and physics to study biodiversity-climate-human interactions. Research Focus : Phenological Rhythms in Ecosystems AI & Mathematical Modeling of Climate Impacts Earth System Data from Satellites and Citizen Science Scientific Contributions : DeepExtremeCubes methodology for climate extremes SpectralIndices.Jl software for remote sensing Plant Trait Coordination and Forest Monitoring Innovations Awards : Forschungspreis (Paderborn University, 2015) Technion Postdoctoral Scholarship (2014-15) Funding for EWM Summer School Course (2013) Education : PhD in Applied Mathematics (University of Bath, 2014) Master of Mathematics (University of Reading, 2008) Outreach : MDR Wissen Radio Contributions Royal Society Summer Science Exhibition Co-organizer of EuropaBON Stakeholder Dashboard